年間 6 号発行
ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099
Indexed in
REDUCED ORDER MODELING FOR NONLINEAR MULTI-COMPONENT MODELS
要約
Reduced order modeling plays an indispensible role in most real-world complex models. A hybrid application of order reduction methods, introduced previously, has been shown to effectively reduce the computational cost required to find a reduced order model with quantifiable bounds on the reduction errors, which is achieved by hybridizing the application of local variational and global sampling methods for order reduction. The method requires the evaluation of first-order derivatives of pseudo-responses with respect to input parameters and the ability to perturb input parameters within their user-specified ranges of variations. The derivatives are employed to find a subspace that captures all possible response variations resulting from all possible parameter variations with quantifiable accuracy. This paper extends the applicability of this methodology to multi-component models. This is achieved by employing a hybrid methodology to enable the transfer of sensitivity information between the various components in an efficient manner precluding the need for a global sensitivity analysis procedure, which is often envisaged to be computationally intractable. Finally, we introduce a new measure of conditioning for the subspace employed for order reduction. Although, the developments are general, they are applied here to smoothly behaving functions only. Extension to non-smooth functions will be addressed in a future article. In addition to introducing these new developments, this manuscript is intended to provide a pedagogical overview of our current developments in the area of reduced order modeling to real-world engineering models.
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Huang Dongli, Abdel-Khalik Hany, Rabiti Cristian, Gleicher Frederick, Dimensionality reducibility for multi-physics reduced order modeling, Annals of Nuclear Energy, 110, 2017. Crossref
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Mandelli D., Maljovec D., Alfonsi A., Parisi C., Talbot P., Cogliati J., Smith C., Rabiti C., Mining data in a dynamic PRA framework, Progress in Nuclear Energy, 108, 2018. Crossref
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Mandelli D., Parisi C., Alfonsi A., Maljovec D., Boring R., Ewing S., St Germain S., Smith C., Rabiti C., Rasmussen M., Multi-unit dynamic PRA, Reliability Engineering & System Safety, 185, 2019. Crossref
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Foad Basma, Yamamoto Akio, Endo Tomohiro, Uncertainty and regression analysis of the MSLB accident in PWR based on unscented transformation and low rank approximation, Annals of Nuclear Energy, 143, 2020. Crossref
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Mandelli Diego, Parisi Carlo, Anderson Nolan, Ma Zhegang, Zhang Hongbin, Dynamic PRA Methods to Evaluate the Impact on Accident Progression of Accident Tolerant Fuels, Nuclear Technology, 207, 3, 2021. Crossref
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Foad Basma, Novog David R., Implementation and testing of unscented transformation and low rank approximation to enhance SCALE code uncertainty calculations, Annals of Nuclear Energy, 167, 2022. Crossref
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Foad Basma, Elzohery Rabab, Novog David R., Demonstration of combined reduced order model and deep neural network for emulation of a time-dependent reactor transient, Annals of Nuclear Energy, 171, 2022. Crossref
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Anghel C.V., Deng D.S., Golesorkhi S., Shreeves P., Bingham D., Trottier A., Emulating loss of coolant simulations in a pressurized heavy water reactor, Annals of Nuclear Energy, 178, 2022. Crossref